58,567 research outputs found
Binary Message Passing Decoding of Product Codes Based on Generalized Minimum Distance Decoding
We propose a binary message passing decoding algorithm for product codes
based on generalized minimum distance decoding (GMDD) of the component codes,
where the last stage of the GMDD makes a decision based on the Hamming distance
metric. The proposed algorithm closes half of the gap between conventional
iterative bounded distance decoding (iBDD) and turbo product decoding based on
the Chase--Pyndiah algorithm, at the expense of some increase in complexity.
Furthermore, the proposed algorithm entails only a limited increase in data
flow compared to iBDD.Comment: Invited paper to the 53rd Annual Conference on Information Sciences
and Systems (CISS), Baltimore, MD, March 2019. arXiv admin note: text overlap
with arXiv:1806.1090
On practical design for joint distributed source and network coding
This paper considers the problem of communicating correlated information from multiple source nodes over a network of noiseless channels to multiple destination nodes, where each destination node wants to recover all sources. The problem involves a joint consideration of distributed compression and network information relaying. Although the optimal rate region has been theoretically characterized, it was not clear how to design practical communication schemes with low complexity. This work provides a partial solution to this problem by proposing a low-complexity scheme for the special case with two sources whose correlation is characterized by a binary symmetric channel. Our scheme is based on a careful combination of linear syndrome-based Slepian-Wolf coding and random linear mixing (network coding). It is in general suboptimal; however, its low complexity and robustness to network dynamics make it suitable for practical implementation
Binary Message Passing Decoding of Product-like Codes
We propose a novel binary message passing decoding algorithm for product-like
codes based on bounded distance decoding (BDD) of the component codes. The
algorithm, dubbed iterative BDD with scaled reliability (iBDD-SR), exploits the
channel reliabilities and is therefore soft in nature. However, the messages
exchanged by the component decoders are binary (hard) messages, which
significantly reduces the decoder data flow. The exchanged binary messages are
obtained by combining the channel reliability with the BDD decoder output
reliabilities, properly conveyed by a scaling factor applied to the BDD
decisions. We perform a density evolution analysis for generalized low-density
parity-check (GLDPC) code ensembles and spatially coupled GLDPC code ensembles,
from which the scaling factors of the iBDD-SR for product and staircase codes,
respectively, can be obtained. For the white additive Gaussian noise channel,
we show performance gains up to dB and dB for product and
staircase codes compared to conventional iterative BDD (iBDD) with the same
decoder data flow. Furthermore, we show that iBDD-SR approaches the performance
of ideal iBDD that prevents miscorrections.Comment: Accepted for publication in the IEEE Transactions on Communication
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